Why Shunya is a stronger choice than Cartesia for production text-to-speech (TTS)

Cartesia has built a strong reputation around real-time voice AI. Its Sonic family is designed for fast, natural speech generation, with a particular focus on conversational applications, voice agents, low latency, and efficient streaming.

Cartesia’s current pricing page lists Sonic-3.6 as its TTS model, while its documentation highlights capabilities such as natural expressive speech, accurate transcript following, multilingual support, voice cloning, pronunciation dictionaries, and streaming through WebSocket, SSE, or HTTP.

That makes Cartesia a good choice for real-time voice agents.

Shunya approaches TTS from a broader production perspective.

Shunya Zero TTS combines natural and expressive voices, global and Asian language coverage, dedicated Indic voices, cross-lingual synthesis, voice cloning, streaming, and enterprise voice applications. Its current documentation lists 46 voices, 11 expression styles, and voice cloning from a short reference clip.

The real comparison is therefore not simply which model is faster.

It is:

Which TTS platform gives your application the right combination of voice quality, pronunciation, latency, language capability, voice identity, deployment, and production infrastructure?

Shunya and Cartesia at a glance

Capability	Shunya Zero TTS	Cartesia Sonic
Language focus	Global, Indian and Asian languages	Global multilingual
Indic specialization	Core focus	Growing coverage
Documented voice catalogue	46 voices	Voice library + custom voices
Indic voice catalogue	23 Indic languages + English	Selected Indic languages
Expression	11 styles	Emotion and generation controls
Voice cloning	Yes	Yes
Cross-lingual voice	Yes	Voice localization available
Streaming	Yes	Yes
Real-time focus	Yes	Core strength
Pronunciation controls	Yes	Yes, including pronunciation dictionaries
Telephony formats	μ-law, A-law and more	Supported
Voice-agent integration	Broader STT + TTS + agent stack	Core strength
On-device options	Yes	Yes
Enterprise deployment	Cloud, private, on-premise, edge	Cloud, on-premise, on-device
Best fit	Enterprise multilingual voice AI	Real-time conversational voice

Cartesia’s current documentation recommends Sonic 3.5 for most TTS use cases, while its latest pricing page lists Sonic-3.6.

Cartesia is built around real-time interaction

One of Cartesia’s clearest strengths is its focus on interactive voice.

Its documentation provides three streaming options:

HTTP bytes

Server-Sent Events

WebSocket

All stream audio as it is generated. WebSockets support long-lived sessions, text arriving incrementally, timestamps, and multiple utterances on the same connection.

That architecture is particularly useful for:

Voice agents

Assistants

Games

Telephony

Interactive applications

A voice agent may not know the entire response before generation begins.

The LLM may produce:

“Sure, I can help you with that…”

and continue generating the rest.

A TTS system that can begin speaking while the response is still being generated can make the interaction feel substantially faster.

Cartesia has designed its TTS around exactly this use case.

Shunya is also designed for conversational TTS

Shunya’s TTS documentation provides streaming synthesis over WebSockets, with a persistent session that can stream audio chunks back as speech is generated.

This is important because Shunya’s TTS is also designed for voice agents and conversations, not just offline narration.

The difference lies in the broader platform.

Shunya’s Voice Agent connects STT, reasoning, and TTS, while the broader Platform combines speech recognition, speech synthesis, voice agents, SLMs, and edge speech intelligence.

Cartesia has made real-time interaction itself the centerpiece of its platform.

Shunya’s proposition is broader:

Generate speech → understand speech → reason → generate speech

That can matter when TTS is part of a larger enterprise voice system.

Latency is not just one number

TTS latency can mean several different things.

Time to first byte

When the server starts returning audio.

Time to first audio

When the user can actually hear the first sound.

Time to complete

How long until the full response is generated.

Cartesia’s documentation explicitly discusses this distinction and recommends WebSocket connections for long-lived real-time sessions because they avoid repeatedly paying TCP/TLS connection overhead.

This is an important engineering advantage.

Shunya provides WebSocket streaming as well, allowing persistent real-time synthesis sessions.

So instead of comparing one latency number from each vendor, benchmark the actual experience:

User stops speaking → LLM produces text → TTS starts → user hears the response

That is end-to-end conversational latency.

For voice agents, this matters much more than isolated model inference speed.

Voice quality: naturalness, pacing and context

Cartesia’s current Sonic documentation emphasizes natural, expressive speech, strong pacing, emotional expression, and accurate transcript following.

Cartesia also highlights natural alphanumeric handling, including confirmation codes, order numbers, phone numbers, IDs, and emails.

This is particularly relevant for enterprise voice systems.

Shunya’s TTS product similarly focuses on natural pacing, pauses, emphasis, pronunciation, and conversational delivery. Its current product positioning is specifically around voices for conversations rather than narration alone.

Shunya also provides 11 expression styles, including Conversational, Narrative, News, Happy, Sad, and Enthusiastic.

Both platforms therefore have a serious quality story.

The more important question is how each handles difficult production text.

Difficult text is one of the best ways to evaluate TTS

Try asking either model to speak:

“Your payment of ₹1,25,499.50 has been received.”

Then:

“Your reference ID is AB-2047-X.”

Then:

“Your appointment is scheduled for 12 October at 10:30 AM.”

These reveal problems that a simple:

“Hello, how can I help you today?”

will never expose.

Cartesia explicitly highlights its improved handling of confirmation codes, order numbers, phone numbers, IDs, and emails in Sonic 3.5.

Shunya’s TTS API provides language selection, speed controls, silence trimming, multiple audio formats, and pronunciation-aware processing, which are useful when building production voice workflows.

For enterprise TTS, test:

Numbers

Currency

Dates

Acronyms

Names

URLs

Product codes

Medical terminology

That is often a better quality benchmark than a generic “human likeness” score.

Indian voices are a key Shunya differentiator

Cartesia supports several Indian languages, including Hindi, Tamil, Bengali, Telugu, Gujarati, Kannada, Malayalam, and Marathi in its current Sonic language set.

But Shunya takes a more dedicated approach to Indic TTS.

The Zero TTS Indic model provides 46 voices across 23 Indic languages plus English, with dedicated male and female voices.

Shunya’s documentation also states that any available voice can speak any supported language, allowing the vocal identity to be separated from the output language.

That is useful when building a multilingual Indian assistant.

For example:

English voice identity

↓

Hindi

↓

Tamil

↓

Telugu

without forcing the brand to use an entirely different assistant persona for every language.

This makes Indian multilingual voice identity one of the clearest areas where Shunya has a differentiated story.

Cross-lingual voice identity matters for enterprise brands

Imagine a telecom provider with one virtual assistant.

A customer starts in English.

Then switches to Hindi.

Later, another customer speaks Tamil.

The system should ideally still sound like the same assistant.

Shunya’s cross-lingual synthesis architecture allows the same voice identity to be used across supported languages.

Cartesia also supports localization of cloned voices into other languages, allowing voice identities to be adapted across its supported language set.

So again, both offer the capability.

The meaningful benchmark is:

How consistently does the same voice sound across languages?

For a global or Indian brand, that can be more important than the number of voices available.

Voice cloning: both have strong options

Cartesia supports voice cloning through its Clone Voice API and documents different approaches for high similarity and stability. It recommends short, clean recordings for high-similarity cloning and supports localization of voices into other languages.

Shunya supports voice cloning from a short reference clip, with the cloned voice usable across its Indic language voice stack.

The important enterprise questions are:

How much reference audio is needed?

How similar is the clone?

How stable is it over long generations?

Does the identity survive translation or localization?

Does it remain natural over a phone call?

Both platforms should be evaluated using the same source voice and identical scripts.

And, as with any voice-cloning deployment, organizations should ensure they have the necessary consent and rights to use the source voice.

Expression and emotional control

Cartesia’s Sonic family offers controls around emotion and generation behavior, and earlier Sonic models exposed volume, speed, and emotion controls through API parameters and SSML-style controls.

Shunya offers 11 explicit expression styles, including:

Happy

Sad

Angry

Fearful

Surprised

News

Conversational

Narrative

Enthusiastic

This is a difference in control philosophy.

Cartesia gives developers fine-grained generation controls.

Shunya gives developers a set of production-oriented delivery styles.

For a creative application where you want to manipulate every aspect of performance, Cartesia’s lower-level controls can be attractive.

For an enterprise assistant where you want predictable styles such as Conversational, News, or Enthusiastic, Shunya’s simpler style system can be easier to operationalize.

Telephony is an important production test

Both platforms support real-time audio workflows, but enterprise telephony introduces additional requirements.

You may need:

8 kHz output

μ-law

A-law

Fast streaming

Low buffering

Stable speech over compressed audio

Shunya’s TTS API supports μ-law, A-law, PCM, WAV, MP3, OGG Opus, and FLAC.

Cartesia’s streaming endpoints are designed for assistants, telephony-style stacks, and other long-lived conversational sessions, with WebSockets recommended when text arrives incrementally.

For IVR and contact-center systems, benchmark the actual call.

A voice that sounds excellent through studio headphones can behave very differently after 8 kHz telephony encoding.

The broader voice stack matters

Cartesia has expanded from TTS into a broader real-time voice platform.

Its current website presents:

Sonic

Text-to-speech

Ink

Speech-to-text

Managed Agents

Voice-agent development and deployment

The company describes the system as a full stack for interactive intelligence and emphasizes deployment across cloud, on-premise, and on-device environments.

Shunya takes a similarly broad direction.

The Shunya Platform combines:

STT

TTS

Voice agents

Custom SLMs

Edge speech understanding

Knowledge-grounded AI

This makes both vendors more than standalone TTS providers.

The distinction is in positioning.

Cartesia is highly focused on interactive intelligence and low-latency voice agents.

Shunya’s stack adds a stronger Indic and multilingual enterprise layer around that voice architecture.

Pricing: Cartesia has a clear usage model

Cartesia’s current pricing is subscription-based with included credits.

The public plans currently include:

Free: $0/month with 20K credits

Pro: $5/month with 100K credits

Startup: $49/month with 1.25M credits

Scale: $299/month with 8M credits

Enterprise: custom pricing

The current pricing page lists Sonic-3.6 as the TTS model, with concurrency increasing from 2 concurrent requests on Free to 15 on Scale, with custom Enterprise limits.

Shunya’s pricing is structured differently, with enterprise usage and model-specific pricing.

For TTS, the important comparison is not simply subscription price.

Calculate:

Cost per minute

Cost per million characters

Concurrency

Voice cloning

Streaming

Required deployment tier

Downstream voice-agent costs

A provider with a lower headline price can become more expensive if your application requires a higher tier to achieve production concurrency.

Where Cartesia is the better choice

Cartesia is particularly compelling for:

Real-time voice agents

Its entire platform is built around interactive voice and low-latency generation.

Fast streaming

WebSocket sessions are designed for incremental text and low-latency multi-turn interactions.

Natural conversational speech

Sonic is designed specifically for conversational and support-style transcripts.

Fine-grained generation controls

Cartesia exposes detailed model controls and pronunciation dictionaries.

Alphanumeric text

Sonic 3.5 specifically highlights natural handling of IDs, codes, phone numbers, and emails.

Voice-agent infrastructure

Cartesia’s platform includes STT, TTS, and managed agents.

For a company primarily building real-time conversational agents, Cartesia is a very strong option.

Where Shunya makes a stronger case

Shunya becomes particularly compelling when:

Indian voices are central to the application

Use Zero TTS Indic for a dedicated Indic voice catalogue.

You need global, Indian, and Asian voices

Shunya’s TTS stack is designed around these markets rather than a primarily Western-language voice ecosystem.

One voice needs to work across languages

Shunya supports cross-lingual synthesis, keeping voice identity separate from the spoken language.

You need production-focused expression styles

Shunya provides 11 explicit delivery styles for conversational and business use.

Telephony is important

Shunya supports μ-law and A-law among its audio output formats.

TTS is part of a complete voice-AI stack

The Shunya Platform connects STT, TTS, voice agents, SLMs, edge intelligence, and knowledge-grounded AI.

You need edge-oriented voice applications

Shunya On-Device Models provide a path toward local inference.

Shunya vs Cartesia by use case

Use case	Stronger fit
Real-time voice agents	Cartesia / Both
Conversational TTS	Both
Low-latency streaming	Cartesia
Creative narration	Both
Indian-language TTS	Shunya
Indian multilingual voice agents	Shunya
Global multilingual voice agents	Both
Cross-lingual brand voice	Shunya / Both
Alphanumeric-heavy enterprise TTS	Both
Telephony	Both
Voice cloning	Both
Fine-grained generation controls	Cartesia
Enterprise Indic voice catalogue	Shunya
Broader STT + TTS + voice-AI stack	Both
Edge voice applications	Shunya / Both

How to benchmark Shunya and Cartesia

Use the same text and equivalent voice profiles.

Your test set should contain:

Conversational responses

Questions

Numbers

Currency

Dates

Names

Acronyms

URLs

Product codes

Indian-language sentences

Code-switched sentences

Then score:

Naturalness

Does the voice sound human over both short and long interactions?

Pronunciation

Are names, numbers, places, acronyms, and domain terms correct?

Prosody

Does the voice pause and emphasize naturally?

Expressiveness

Does it match the intended emotional tone?

Voice consistency

Does the same voice remain stable across repeated generations?

Time to first audio

How quickly does the user hear the first response?

End-to-end latency

For a voice agent, how quickly does the system respond after the user finishes speaking?

Telephony quality

How does the voice sound after the audio is encoded for a phone call?

Cost at scale

What does the system cost at your actual monthly volume and concurrency?

This produces a much more useful evaluation than listening to one polished demo.

Final words

Cartesia is a strong production TTS platform with a clear focus on real-time interaction.

Its current Sonic stack emphasizes natural conversational speech, low-latency streaming, pronunciation controls, voice cloning, multilingual generation, and tight integration with voice agents. Cartesia’s broader platform now brings STT, TTS, and managed voice agents together.

Shunya takes a broader production voice approach.

Its TTS stack combines global, Indian, and Asian voice coverage, dedicated Indic voices, cross-lingual synthesis, voice cloning, 11 expression styles, streaming, and telephony-ready formats. Its documentation currently lists 46 voices across 23 Indic languages plus English.

The strongest Shunya differentiator is not simply language coverage.

It is the combination of:

Indic voice specialization

Cross-lingual voice identity

Production-oriented expression

Telephony support

Voice cloning

and a broader platform connecting STT, TTS, speech intelligence, voice agents, SLMs, and edge AI.

Cartesia may be the stronger choice when the primary requirement is ultra-low-latency conversational voice and real-time agent infrastructure.

Shunya becomes particularly compelling when the application needs Indian and Asian voices, multilingual brand identity, enterprise telephony, and a broader speech-to-voice-AI stack.

The best way to decide is simple:

Take the text your application actually generates and make both systems speak it.

Test the names.

Test the numbers.

Test the currency.

Test the Indian languages.

Test the code-switching.

Test a five-minute conversation.

Then measure first-audio latency, pronunciation, naturalness, consistency, and cost.

Because the best TTS model isn’t the one that sounds impressive in a demo.

It’s the one that still sounds right when it becomes part of your product.

Frequently asked questions

Is Shunya better than Cartesia?

It depends on the use case. Cartesia is particularly strong for real-time voice agents, low-latency streaming, and conversational TTS. Shunya is particularly strong for Indic voice applications, cross-lingual voice identity, enterprise telephony, and broader voice-AI infrastructure.

Which has more voice choices?

The exact voice catalogue changes over time. Cartesia maintains a growing voice library, while Shunya’s documentation lists 46 voices across 23 Indic languages plus English.

Does Cartesia support Indian languages?

Yes. Sonic supports Indian languages including Hindi, Tamil, Bengali, Telugu, Gujarati, Kannada, Malayalam, Marathi, and Punjabi.

Does Shunya support Indian languages?

Yes. Shunya’s Zero TTS Indic model provides dedicated voices across 23 Indic languages plus English.

Does Cartesia support voice cloning?

Yes. Cartesia provides voice cloning through its Clone Voice API and also supports voice localization.

Does Shunya support voice cloning?

Yes. Shunya supports cloning from a short reference clip and cross-lingual synthesis.

Which has lower latency?

Cartesia has a particularly strong low-latency positioning for conversational TTS and persistent WebSocket sessions. Shunya also supports real-time WebSocket streaming. The best comparison is time to first audible response in the complete application, not isolated model latency.

Does Cartesia support difficult text such as IDs and phone numbers?

Yes. Sonic 3.5 specifically highlights natural handling of confirmation codes, order numbers, phone numbers, IDs, and emails.

Does Shunya support telephony?

Yes. Shunya supports μ-law and A-law alongside other audio formats.

Which is better for voice agents?

Both are strong options. Cartesia is particularly optimized around real-time voice-agent infrastructure, while Shunya combines voice agents with STT, TTS, speech intelligence, SLMs, and knowledge-grounded AI.

Which is better for creative control?

Cartesia offers fine-grained model controls and pronunciation dictionaries, while Shunya provides 11 predefined expression styles designed for conversational and enterprise delivery.

Which is cheaper?

Cartesia currently offers subscription plans with included TTS credits, starting with a free tier and moving through Pro, Startup, Scale, and Enterprise. Shunya uses its own usage and enterprise pricing structure. The best comparison should use your actual monthly volume and concurrency.

How should I compare Shunya and Cartesia?

Use the same production text and compare naturalness, pronunciation, prosody, expression, voice consistency, first-audio latency, telephony quality, multilingual performance, and total cost at your expected scale.